{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment-analysis-movie-reviews\n",
    "\n",
    "# [Link to my Youtube Video Explaining this whole Notebook](https://www.youtube.com/watch?v=m9ZHNDzMR0Y&list=PLxqBkZuBynVQEvXfJpq3smfuKq3AiNW-N&index=2)\n",
    "\n",
    "[![Imgur](https://imgur.com/92Cnoqv.png)](https://www.youtube.com/watch?v=m9ZHNDzMR0Y&list=PLxqBkZuBynVQEvXfJpq3smfuKq3AiNW-N&index=2)\n",
    "---\n",
    "\n",
    "### [Dataset in Kaggle](https://www.kaggle.com/datasets/hassanamin/sentimentanalysismoviereviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>of escapades demonstrating the adage that what...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>of</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>escapades demonstrating the adage that what is...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>escapades</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>demonstrating the adage that what is good for ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhraseId  SentenceId                                             Phrase  \\\n",
       "0         1           1  A series of escapades demonstrating the adage ...   \n",
       "1         2           1  A series of escapades demonstrating the adage ...   \n",
       "2         3           1                                           A series   \n",
       "3         4           1                                                  A   \n",
       "4         5           1                                             series   \n",
       "5         6           1  of escapades demonstrating the adage that what...   \n",
       "6         7           1                                                 of   \n",
       "7         8           1  escapades demonstrating the adage that what is...   \n",
       "8         9           1                                          escapades   \n",
       "9        10           1  demonstrating the adage that what is good for ...   \n",
       "\n",
       "   Sentiment  \n",
       "0          1  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  \n",
       "5          2  \n",
       "6          2  \n",
       "7          2  \n",
       "8          2  \n",
       "9          2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_df = pd.read_csv('../input/sentiment-analysis-movie-reviews/train.tsv', sep='\\t')\n",
    "train_df = pd.read_csv('../input/sentiment-analysis-movie-reviews/train.tsv', sep='\\t', nrows=1000)\n",
    "\n",
    "''' While reading tsv If you have a header, you can pass header=0.\n",
    "\n",
    "pd.read_csv('c:/~/trainSetRel3.txt', sep='\\t', header=0) '''\n",
    "\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_sentiment_to_df(df):\n",
    "    sentiment_tuple = []\n",
    "\n",
    "    for x in range(0, df.shape[0]):\n",
    "        QuantTextBlob = TextBlob(df.iloc[x][2])\n",
    "        measures = QuantTextBlob.sentiment\n",
    "        sentiment_tuple.append(measures)\n",
    "    df['Text Score'] = sentiment_tuple\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Text Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>1</td>\n",
       "      <td>(0.39999999999999997, 0.38125000000000003)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.7, 0.6000000000000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>36</td>\n",
       "      <td>heroes</td>\n",
       "      <td>3</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>36</td>\n",
       "      <td>of horror movies</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>36</td>\n",
       "      <td>horror movies</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>36</td>\n",
       "      <td>horror</td>\n",
       "      <td>1</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>try to avoid</td>\n",
       "      <td>1</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PhraseId  SentenceId                                             Phrase  \\\n",
       "0           1           1  A series of escapades demonstrating the adage ...   \n",
       "1           2           1  A series of escapades demonstrating the adage ...   \n",
       "2           3           1                                           A series   \n",
       "3           4           1                                                  A   \n",
       "4           5           1                                             series   \n",
       "..        ...         ...                                                ...   \n",
       "995       996          36                                             heroes   \n",
       "996       997          36                                   of horror movies   \n",
       "997       998          36                                      horror movies   \n",
       "998       999          36                                             horror   \n",
       "999      1000          36                                       try to avoid   \n",
       "\n",
       "     Sentiment                                  Text Score  \n",
       "0            1  (0.39999999999999997, 0.38125000000000003)  \n",
       "1            2                   (0.7, 0.6000000000000001)  \n",
       "2            2                                  (0.0, 0.0)  \n",
       "3            2                                  (0.0, 0.0)  \n",
       "4            2                                  (0.0, 0.0)  \n",
       "..         ...                                         ...  \n",
       "995          3                                  (0.0, 0.0)  \n",
       "996          2                                  (0.0, 0.0)  \n",
       "997          2                                  (0.0, 0.0)  \n",
       "998          1                                  (0.0, 0.0)  \n",
       "999          1                                  (0.0, 0.0)  \n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = add_sentiment_to_df(train_df)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create another, new column that prints *only* the polarity score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_polarity_to_df(df):\n",
    "    polarity_list = []\n",
    "\n",
    "    for x in range(0, df.shape[0]):\n",
    "        QuantTextBlob = TextBlob(df.iloc[x][2])\n",
    "        measures = QuantTextBlob.sentiment.polarity\n",
    "        polarity_list.append(measures)\n",
    "    df['Text Polarity'] = polarity_list\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Text Score</th>\n",
       "      <th>Text Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>1</td>\n",
       "      <td>(0.39999999999999997, 0.38125000000000003)</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.7, 0.6000000000000001)</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhraseId  SentenceId                                             Phrase  \\\n",
       "0         1           1  A series of escapades demonstrating the adage ...   \n",
       "1         2           1  A series of escapades demonstrating the adage ...   \n",
       "2         3           1                                           A series   \n",
       "3         4           1                                                  A   \n",
       "4         5           1                                             series   \n",
       "\n",
       "   Sentiment                                  Text Score  Text Polarity  \n",
       "0          1  (0.39999999999999997, 0.38125000000000003)            0.4  \n",
       "1          2                   (0.7, 0.6000000000000001)            0.7  \n",
       "2          2                                  (0.0, 0.0)            0.0  \n",
       "3          2                                  (0.0, 0.0)            0.0  \n",
       "4          2                                  (0.0, 0.0)            0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = add_polarity_to_df(train_df)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vader "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /home/paul/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.sentiment import SentimentIntensityAnalyzer as vad\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>of escapades demonstrating the adage that what...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>of</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>escapades demonstrating the adage that what is...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>escapades</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>demonstrating the adage that what is good for ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhraseId  SentenceId                                             Phrase  \\\n",
       "0         1           1  A series of escapades demonstrating the adage ...   \n",
       "1         2           1  A series of escapades demonstrating the adage ...   \n",
       "2         3           1                                           A series   \n",
       "3         4           1                                                  A   \n",
       "4         5           1                                             series   \n",
       "5         6           1  of escapades demonstrating the adage that what...   \n",
       "6         7           1                                                 of   \n",
       "7         8           1  escapades demonstrating the adage that what is...   \n",
       "8         9           1                                          escapades   \n",
       "9        10           1  demonstrating the adage that what is good for ...   \n",
       "\n",
       "   Sentiment  \n",
       "0          1  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  \n",
       "5          2  \n",
       "6          2  \n",
       "7          2  \n",
       "8          2  \n",
       "9          2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('../input/sentiment-analysis-movie-reviews/train.tsv', sep='\\t', nrows=1000)\n",
    "\n",
    "''' While reading tsv If you have a header, you can pass header=0.\n",
    "\n",
    "pd.read_csv('c:/~/trainSetRel3.txt', sep='\\t', header=0) '''\n",
    "\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vader = train_df.copy()\n",
    "sentiment = vad()\n",
    "\n",
    "# Making additional columns for sentiment score in the vader dataframe\n",
    "sen=['Positive','Negative','Neutral']\n",
    "sentiments = [sentiment.polarity_scores(i) for i in vader['Phrase'].values]\n",
    "vader['Negative Score']=[i['neg'] for i in sentiments]\n",
    "vader['Positive Score'] = [i['pos'] for i in sentiments]\n",
    "vader['Neutral Score'] = [i['neu'] for i in sentiments]\n",
    "vader['Compound Score'] = [i['compound'] for i in sentiments]\n",
    "score = vader['Compound Score'].values\n",
    "\n",
    "t = []\n",
    "\n",
    "for i in score:\n",
    "    if i >= 0.05 :\n",
    "        t.append('Positive')\n",
    "    elif i <= -0.05:\n",
    "        t.append('Negative')\n",
    "    else:\n",
    "        t.append('Neutral')\n",
    "vader['Overall Sentiment'] = t\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
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       "      <th>Positive Score</th>\n",
       "      <th>Neutral Score</th>\n",
       "      <th>Compound Score</th>\n",
       "      <th>Overall Sentiment</th>\n",
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       "      <td>0.157</td>\n",
       "      <td>0.843</td>\n",
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       "      <td>Positive</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.000</td>\n",
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       "   PhraseId  SentenceId                                             Phrase  \\\n",
       "0         1           1  A series of escapades demonstrating the adage ...   \n",
       "1         2           1  A series of escapades demonstrating the adage ...   \n",
       "2         3           1                                           A series   \n",
       "3         4           1                                                  A   \n",
       "4         5           1                                             series   \n",
       "\n",
       "   Sentiment  Negative Score  Positive Score  Neutral Score  Compound Score  \\\n",
       "0          1             0.0           0.157          0.843          0.5579   \n",
       "1          2             0.0           0.195          0.805          0.4404   \n",
       "2          2             0.0           0.000          1.000          0.0000   \n",
       "3          2             0.0           0.000          0.000          0.0000   \n",
       "4          2             0.0           0.000          1.000          0.0000   \n",
       "\n",
       "  Overall Sentiment  \n",
       "0          Positive  \n",
       "1          Positive  \n",
       "2           Neutral  \n",
       "3           Neutral  \n",
       "4           Neutral  "
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "vader.head()"
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  {
   "cell_type": "code",
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    "import plotly.express as px\n",
    "\n",
    "fig = px.histogram(data_frame=vader, x='Compound Score', color='Overall Sentiment', template='plotly')\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:\n",
      "\n",
      "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "\n"
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    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Overall Sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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4lcvelGT+1iw7l2zP/bWF7fzlRtP/sr23sSMwFEbj51V1SFX9BnA/8IaZLFxV/6+qjmuThwDHDMxbWVVnbreeajq/AF61nX4h7wNMGwpJdpgbPXZy23N/bc7DQqGqXjji7Y2FoTB6XwGelmS/JJ9OsibJlUmeBZDkxe2oYnWSbyd5fJJF7ShjT+BdwIlt/olJXp/kg0n2TnJzkt3aevZKckuSPZI8NcllSa5K8pUkzxzj598ZPUB398mfbzwjyUSSTyT5Znsd3urPSPLWgXbXJFkEnAk8te2/9yY5ou2TlcB1re2n2766tj2xr5nZmv01keTy9jP/UPu/NL/Ne8T+SHIm8Ji2Hz/W6u5p7xcm+b2BbX40yXFJdm/7/Jvt//2fjPwnsT1Ula/t/ALuae/zgEuBPwU+ALyj1R8JrG7lzwCHt/Lj2jKLgGta3euBDw6su59u635JK58IfKiVVwGLW/n5wBXj/pnsTC/gHuAJwE3A3sBbgTPavH8EXtTKTwGub+UzgLcOrOOath/7fdnqjwB+Bhw0ULdfe39MW+6JbfomYP64fx47+msr99cHgbe38suAmvpZb2Z/3LPxdtv7K4EVrbwncEtbdjnwV63+UcDk4H7fUV8evo7GY5KsbuWvAOcDXwf+AKCqrkjyxCRPAL4K/G376+OTVXVrkmG3cxFdGHyR7mG//5HkccALgY8PrOdR2/6R5paq+kmSC4A3AT8fmPU7wMEDP9sntJ/5THyjqn4wMP2mJK9s5QOBxcAdW9HtOWsr9teL6H6ZU1WXJblzYJmZ7o9/Bs5J8ii6gPlyVf08ydHAs9KN6wZdYC0GfrCJ9ewQDIXR+HlVHTJYsalf9FV1ZpL/SXfd4KtJXgrcN+R2VgJ/k2Q/4LnAFcBewF0bb19b5X3At4CPDNTtBhxWVQ/bR0ke4OGnYx+9mfX+bGC5I+h+cb2gqu5N8qUtLKtNex/D769pV7A1+6Oq7mvtXkr3R9qFU6sDTquqz8/sY4yX1xRmz1eA10L/D+9H7a+bp1bV1VX1HrqhPjY+//9T4PHTrbCq7mnLnAN8tqoerKqfAD9IcnzbVpI8exQfaFdXVT8GLgZOGaj+AnDa1ESSQ1rxJuDQVncocFCr3+T+a/YG7my/gJ4JHLY9+j4XzXB/fRU4odUdDezb6je3P36ZZI9NbP4i4GTgt4DLWt3ngT+dWibJ05PstXWfbvYYCrPnDOC5SdbQXXxc1urf3C5KrgF+SXcoOuiLdIe/q5OcOM16LwJe196nvBY4Jcl3gGvxeym2xVl0wyhPeROwpF04vI6H7iz7BLBfkmuBNwL/F6Cq7qA7ArwmyXunWf9lwLwk19P9u7hyRJ9jrhh2f70TODrJNcDxwO10Ab65/XEesGbqQvNGvgC8GPhf1X0fDMCH6G4m+Fbbzt+zE5ydcZgLSXNOO///YHVjrr0AONdTrp0dPrUkaQSeAlzcbum+H/jjMfdnh+GRgiSp5zUFSVLPUJAk9QwFSVLPUNBOIcnCJJcmuSHJ95Ock25sqFFvd2p8m0XttsKN5++W5P3tltOr2zg3Bz1yTUNt64gkLxyYfkOSk7a+90Nt89gkB49yG9q5GAra4aV7/PSTwKerajHwdLpxot69Hda9rXfgnQg8GXhWVf0m3dAJd23luo6gG6IEgKr6u6q6YBv7tyXHAoaCeoaCdgZHAvdV1UcAqupBuhEx/yjJY9ONOvvrU42TfCnJknQjx344yTfSjUC7tM1/fZKVSa4AViV5XJJVSb7V/tqfycN++wO3VdWvWt9urao723aOTvK1tt6PT42RlO57Et45sL1nphtR9Q3An7cHFX8rAyOvts90dpLJJNcn+XdJPtmOnP564LO/rn3e1Un+Psnurf6eJO9O8p3281rQjkpeAby3tX/qVu0d7VIMBe0Mfh24arCiDefxr8DT6J7mnhqyYH9g/6qaBP4L3QixzwNeQvfLb2qYgUOB46rqxXRjTb2yqg5t7c5qRyfDuBj4/fZL9awkz2n9mA/8FfA7bb2TwFsGlvtRqz+XbnTVm4C/A86u7rs4vjLNtu6vqiWt3aXAqcBvAK9PN8Div6U7cjm8PYj1IG1oFboxsa6sqmcDXwb+uKr+hW78rLe1bX5/yM+sXZgPr2lXcDHdMAPvoAuHS1r90cAr8tD3HDya7qElgMvbWDnQDVz2N0l+G/gVcACwgG7og81qo9o+g+5o5ki6I4/j6YZOPphuiAvohlT+2sCin2zvVwGvGvJzrmzvVwPXVtVtAElupBvN80V0AyN+s23zMcD6tsz9wGcHtvm7Q25Tc4yhoJ3BdcBxgxXphh1/CrC2DV52R7ovLjqRh8a3CfAHVfW9jZZ9PgMjldL9NT0BPLeqfpnkJmYwUmlV/YJuzKp/TvJDuvP0X6ALntdsYrFftPcHGf7/4dQyvxooT03Po/u8K6rq7dMs+8t66EnVmWxTc4ynj7QzWAU8dupOnHae/Czgo1V1b2tzEfAXwN5VtabVfR44bepU0NSpnWnsDaxvgfAS4NeG7ViSQ5M8uZV3A54F3Ew3kNrhSZ7W5u2V5OlbWN2WRlTdklXAcUme1La5X5ItfZZt3aZ2MYaCdnjtL9xXAscnuYFuBNL7ePh35l5C90VDFw/U/TdgD7qRLa9t09P5GN1ImlcDJwHfnUH3ngR8pt2uuobuqyE/WFUb6L4l75/SjYD7NR45LPrGPgO8cupC8wz6AEBVXUd3HeMLbZuX010I35wLgbe1C/FeaJZjH0mSHuKRgiSpZyhIknqGgiSpZyhIknqGgiSpZyhIknqGgiSp9/8BrvuausmpNZEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(vader['Overall Sentiment'])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "36cf16204b8548560b1c020c4e8fb5b57f0e4c58016f52f2d4be01e192833930"
  },
  "kernelspec": {
   "display_name": "Python 3.9.12 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
